In this paper, a sparse representation-based intuitionistic fuzzy clustering (SRIFC) approach is presented for solving the large-scale decision making (LSDM) problem. It consists of two algorithms: the sparse representation-based intuitionistic fuzzy clustering-exactly precision algorithm (which is presented for an exactly precision requirement), and the sparse representation-based intuitionistic fuzzy clustering-soft precision and scalable algorithm (which is proposed for soft precision and scalable requirements). In the proposed SRIFC approach, decision makers are clustered into several interest groups according to their interest preferences and relation sparsity of their intuitionistic fuzzy assessment information. The purpose of the presented SRIFC approach is to investigate the group intra-relations among DMs and to detect the group leaders for each interest group during the clustering process. According to the illustrative experiment results, the presented SRIFC approach is an adaptive and the unsupervised clustering method and presents more robust and efficient for LSDM problems.

中文翻译：

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基于稀疏表示的直觉模糊聚类方法为大规模决策寻找组内关系和组长

在本文中，提出了一种基于稀疏表示的直觉模糊聚类（SRIFC）方法来解决大规模决策（LSDM）问题。它由两种算法组成：基于稀疏表示的直觉模糊聚类-精确精度算法（针对精确精度要求提出）和基于稀疏表示的直觉模糊聚类-软精度和可扩展算法（针对软精度和可扩展算法提出）。精度和可扩展性要求）。在提议的 SRIFC 方法中，决策者根据他们的兴趣偏好和他们的直觉模糊评估信息的关系稀疏性被聚集到几个兴趣组中。所提出的 SRIFC 方法的目的是调查 DM 之间的组内关系，并在聚类过程中检测每个兴趣组的组长。根据说明性实验结果，所提出的 SRIFC 方法是一种自适应的无监督聚类方法，对 LSDM 问题具有更强的鲁棒性和效率。